Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A neural net for blind separation of nonstationary signals
Neural Networks
Independent component analysis for identification of artifacts in magnetoencephalographic recordings
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Analysis of sparse representation and blind source separation
Neural Computation
Blind separation of positive sources by globally convergent gradient search
Neural Computation
One-Bit-Matching Conjecture for Independent Component Analysis
Neural Computation
Extraction of Specific Signals with Temporal Structure
Neural Computation
Blind Source Separation Using Temporal Predictability
Neural Computation
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
Complexity Pursuit: Separating Interesting Components from Time Series
Neural Computation
Learning Overcomplete Representations
Neural Computation
Blind source separation of more sources than mixtures using sparse mixture models
Pattern Recognition Letters
Letters: Nonlinear innovation to blind source separation
Neurocomputing
A fixed-point algorithm for blind source separation with nonlinear autocorrelation
Journal of Computational and Applied Mathematics
Letters: A fast fixed-point algorithm for complexity pursuit
Neurocomputing
Letters: Gaussian moments for noisy complexity pursuit
Neurocomputing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Blind source separation by nonstationarity of variance: a cumulant-based approach
IEEE Transactions on Neural Networks
A "nonnegative PCA" algorithm for independent component analysis
IEEE Transactions on Neural Networks
Hybrid linear and nonlinear complexity pursuit for blind source separation
Journal of Computational and Applied Mathematics
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Independent component analysis (ICA) and blind source separation (BSS) methods have been used for pattern recognition problems. It is well known that ICA and BSS depend on the statistical properties of original sources or components, such as non-Gaussianity. In the paper, using a statistical property-nonlinear autocorrelation and maximizing the nonlinear autocorrelation of source signals, we propose a fast fixed-point algorithm for BSS. We study its convergence property and show that its convergence speed is at least quadratic. Simulations by the artificial signals and the real-world applications verify the efficient implementation of the proposed method.